A mathematically-driven framework for pandemic planning and management

尽管COVID-19是过去20年来第四大全球大流行病（2012-13年的MERS、2009-10年的H1N1、2002-04年的SARS），但COVID-19使人们关注缺乏明确和强大的工具来管理广泛的流行病。该研究项目通过设计最佳缓解策略和疫苗优先政策，满足加拿大公共卫生机构和医疗机构在管理COVID和未来大流行病方面的需求。为了获得这些最佳政策，这项研究利用我们完成的基于COVID代理的模拟模型（ABM），在可推广到任何人群和疾病的框架中进行深入的，基于代理的分析。公共卫生机构主要依赖于高水平的疾病传播预测模型，称为房室模型（通常是传统的易感染-传染-恢复（SIR）模型的扩展），以估计流行病如何在每日病例数，住院人数和死亡人数方面传播。这些模型的好处是它们只需要关于疾病的高级信息，例如，繁殖数（R 0）（单个病例引起的新感染的平均数）和人口，例如，规模和接触率;对最少数据的需求意味着这些模型可以在大流行的早期阶段迅速开发。缺点是只有高层次的信息进入，所以只有高层次的信息出来，使这些模型不适合详细的政策评估。通过包含详细的人口统计、医疗和经济信息，ABM可以更精确地模拟疾病传播，测试和优化细微差别的缓解策略。特别是，我们将个人健康状况（例如，合并症）的区域患病率的基础上，允许新的调查人口的结果，以及公平地占人口的多样性。例如，农村和较贫穷的社会经济地区通常具有更差的健康状况和更高的合并症患病率，以及减少获得医疗保健;这些地区将受到流行病的严重打击，并且没有在房室模型中表示。公共卫生政策制定者根据经验、直觉、有限的数据分析（由于流行病的新奇）以及与建模专家的协商来制定应对新出现的流行病的缓解战略，以回答关于潜在缓解战略的“如果”问题，其中几乎肯定不会包含最佳战略。这项研究超越了这种反应式的方法，通过对ABM及其联系网络的优化和机器学习方法的结合，直接优化政策，而不是以特设的假设方式。此外，这项工作提供了人类可解释的评估政策“杠杆”的有效性，可以由公共卫生官员拉。

COVID-19 has brought into focus the lack of well-defined and robust tools to manage widespread pandemics, despite the fact that COVID is the fourth major global pandemic in the past 20 years (MERS in 2012-13, H1N1 in 2009-10, SARS in 2002-04). This research program addresses the needs of Canada's public health agencies and healthcare institutions in managing COVID and future pandemics by designing optimal mitigation strategies and vaccine prioritization policies. To obtain these optimal policies, this research leverages our completed COVID agent-based simulation model (ABM) for deep, mathematically-driven analysis in a framework generalizable to any population and disease. Public health agencies predominantly rely on high-level disease spread prediction models, called compartmental models (typically extensions of the traditional susceptible-infectious-recovered (SIR) model), to estimate how the pandemic will spread in terms of daily number of cases, hospitalizations, and deaths. The benefit of these models is that they only require high-level information about the disease, e.g., reproduction number (R0) (the average number of new infections caused by a single case), and population, e.g., size and contact rate; the need for minimal data means these models can be rapidly developed in the early stages of a pandemic. The drawback is that only high-level information goes in, so only high-level information comes out, making these models ill-suited for detailed policy assessments. By including detailed population demographic, medical, and economic information, ABMs, where individuals and their unique characteristics are individually represented, can more precisely simulate disease spread and test and optimize nuanced mitigation strategies. In particular, our incorporation of individual health status (e.g., comorbidities) based on regional prevalence allows for novel investigation of population outcomes, as well as equitably accounts for population diversity. For example, rural and poorer socio-economic areas usually have worse health status and higher prevalence of comorbidities, as well as reduced access to healthcare; these areas will be hit harder by pandemics, and are not represented in compartmental models. Mitigation strategies to respond to an emerging pandemic are generated by public health policymakers based on experience, intuition, limited data analysis (due to novelty of the pandemic), and consultation with modelling experts to answer "what if" questions regarding potential mitigation strategies, which almost certainly will not contain the optimal strategy. This research goes beyond this reactive approach by optimizing policies directly, rather than in an ad hoc what-if fashion, through a combination of optimization and machine learning approaches on the ABM and its contact networks. Additionally, this work provides human-interpretable assessments of the effectiveness of policy "levers" that can be pulled by public health officials.